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Image cartoon-texture decomposition by a generalized non-convex low-rank minimization method.
- Source :
-
Journal of the Franklin Institute . Jan2024, Vol. 361 Issue 2, p796-815. 20p. - Publication Year :
- 2024
-
Abstract
- Image cartoon-texture decomposition is an important problem in image processing. In recent years, by exploiting low-rank priors of images, low-rank minimization methods have been widely adopted for image cartoon-texture decomposition. Since matrix rank minimization is an NP-hard problem, the convex nuclear norm is often used as a substitute for the matrix's rank to realize the low-rank minimization methods. In this paper, we utilize a generalized non-convex surrogate of the matrix rank function to develop a novel low-rank minimization model for image cartoon-texture decomposition. We design a proximal alternating algorithm to solve the non-convex model and further demonstrate the global convergence of the algorithm. Numerical experiments illustrate that the proposed method can show much better performances than the existing state-of-the-art methods for image cartoon-texture decomposition. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NP-hard problems
*IMAGE processing
*MATRIX functions
Subjects
Details
- Language :
- English
- ISSN :
- 00160032
- Volume :
- 361
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of the Franklin Institute
- Publication Type :
- Periodical
- Accession number :
- 175031726
- Full Text :
- https://doi.org/10.1016/j.jfranklin.2023.12.025